Device for creating database of alternative normal brain

ABSTRACT

CPU  12  reads out the bloodstream associated values of target voxel of subject&#39;s standardized brain bloodstream images (step S 34 ). CPU  12  sorts the bloodstream associated values in descending order (step S 35 ). CPU  12  rejects bloodstream associated values that are ranked top 10% and bottom 40% (step S 36 ). When the subjects are 20 for example, bloodstream associated values of highest 2 subjects and of lowest 8 subjects are rejected. CPU  12  calculates and stores mean value and standard deviation of remaining bloodstream associated values after the rejection (step S 37 ). CPU  12  calculates mean value and standard deviation of bloodstream associated values for each voxel as target voxel (steps S 31,  S 32,  S 33  and S 38 ). Then, the alternative normal brain database of brain bloodstream image is obtained.

TECHNICAL FIELD

The present invention relates to generate an alternative of normal brain database (hereinafter referred as “alternative normal brain database”) which can be used for diagnosing disease based on functional image of brain such as PET (positron emission tomography) image and SPECT (single photon emission computed tomography) image.

BACKGROUND ART

To diagnose Alzheimer's disease etc., measuring and imaging functional status such as bloodstream or glucose metabolism of each point of patient's brain is carried out. In positron emission tomography (hereinafter referred as “PET”), medical agent which is indicated by positron emission nuclear such as ¹⁸F-FDG is injected to obtain the functional status such as glucose metabolism by measuring gamma ray amount as annihilation radiation at each point of patient's brain. In single photon emission computed tomography (hereinafter referred as “SPECT”), gamma ray emission nuclear species such as ¹²³I and ⁹⁹mTc is used for the same purpose. As shown in FIG. 1, plural sectional images are generated by measuring gamma ray amount of each section of patient's brain. In the sectional image, for example, red, yellow, green and blue are used for showing areas in descending order of status value such as bloodstream or glucose metabolism associated value (voxel value associating functional value measured by PET of SPECT).

Diagnosis of disease can be carried out by comparing data showing status value of normal healthy subject and data showing status value of patient. For comparing, computer displays differential image between the sectional image of normal healthy subject and the sectional image of patient. To obtain the differential image, the sectional image of patient is spatially fitted to that of normal healthy subject and Z-score at each point is calculated. The method to achieve such diagnosis is well known such as 3-Dimentional stereotaxic surface projection (3D-SSP) developed by Minoshima of Washington University, and Statistical Parametoric Mapping (SPM) developed by Friston et al. of Hammersmith Hospital, U.K.

The data showing status value of normal healthy subject comprises mean and standard deviation of status values of each point which are obtained from plural normal healthy subjects. The data showing status value of normal healthy subject are called as normal brain database. The Z-score is obtained by dividing difference between the status value of patient and the status value of normal healthy subject at each point by standard deviation at each point of normal brain database. See equation (1).

Z(x,y,z)=(I _(mean)(x,y,z)−I(x,y,z))/SD(x,y,z)   (1)

Where Z(x,y,z) is Z-score at the point of coordinate x,y,z, I_(mean)(x,y,z) is mean value of status values (voxel values associated to functional status measured by PET of SPECT etc.) at said point of normal healthy subjects, I(x,y,z) is status value at said point of the patient and SD(x,y,z) is standard deviation of status values at the point of normal healthy subjects. I_(mean)(x,y,z) and SD(x,y,z) can be obtained from the normal brain database.

In this method, difference between normal healthy subject and patient can be clearly shown by using the normal brain database having standard deviation.

In 3D-SSP, the biggest status value from brain surface to predetermined depth perpendicular to the brain surface is selected as representative status value and is displayed on the brain surface. Then, Z-score is calculated by comparing the selected status values of patient with that of normal healthy subjects. Images of Z-score are displayed as right-brain lateral surface RT-LAT, left-brain lateral surface LT-LAT, top surface SUP, bottom surface INF, anterior surface ANT, posterior surface POST, right-brain medial surface R-MED and left-brain medial surface L-MED as shown in FIG. 2. In FIG. 2, upper images (denoted “surface”) show status values of brain surface and lower images (denoted “GLB”) show Z-score of brain surface. Z-score image enables to improve detection ability of disease and to assess severity of disease.

Mean value and standard deviation of selected status values of brain surface (said selected biggest values) at each point of brain surface of plural normal healthy subjects should be provided as normal brain database in the 3D-SSP method.

To achieve high diagnosing ability, the normal brain database is made based on preferably at least 10 normal healthy subjects. See Chen W P et al., “Effect of sample size for normal brain database on diagnostic performance of brain FDG PET for the detection of Alzheimer's disease using automated image analysis” Nucl Med Commun. 2008 March; 29(3):270-6.

It is not easy to gather image data of normal healthy subjects, because most of functional brain images such as PET images and SPECT images gathered by the medical center are the functional brain images of subjects who visit medical center and possibly have any disease. Further, functional brain image may vary according to radio isotopes corresponding to disease to be diagnosed and materials indicating them. Therefore, normal brain database should be generated for each combination of radio isotopes and materials indicating them. Above mentioned situations disturb the generation of normal brain database.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a device and method which can easily generate alternative of normal brain database which can be used for alternative for the normal brain database which is difficult to generate. Followings are several independent aspects of the present invention.

-   (1) In accordance with the present invention, a device for     generating alternative of normal brain database to be used for     diagnosing brain disease based on brain status images of subjects     including patients, the alternative of normal brain database showing     normal status of each points of brain, the device comprises: means     for normalizing status value at each point of each brain status     image of subject based on status values of a part or whole of brain     status image of the subject; means for spatially transforming each     brain status image of subject in accordance with anatomical standard     brain; means for rejecting status values presumed as indicating     disease with regard to each points of brain to be used for     diagnosing disease based on said anatomically transformed and     normalized brain status images of subjects; and means for generating     the alternative of normal brain database by obtaining at least mean     status value for said each point of brain to be used for diagnosing     disease based on said anatomically transformed and normalized brain     status images of subjects in which said status values presumed as     indicating disease are rejected,

wherein the rejecting means calculates the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extreme studentized deviate), and rejects at least the status value selected as the ESD when the ESD exceeds a critical value λ calculated based on a predetermined risk α.

It is, therefore, possible to easily form an alternative of a normal brain database with high accuracy based on brain status images including those of patients.

-   (2) In the device for generating alternative of a normal brain     database according to the present invention, rejecting means, for     each points,

i) forms a target status value group consisting of the status values of all the plurality of subjects,

ii) calculates the ESD of the target status value group and stores the ESD as a deviated status value,

iii) determines whether or not the ESD exceeds a critical value λ and stores the result of determination in a correlated manner with the deviated status value,

iv) rejects the deviated status value from the target status value group to form a new target status value group and performs the steps ii) to iv) on the new target status value group when the number of repetitions has not reached a predetermined value, and

v) determines all of the deviated status values determined before the ESD exceed the critical value λ as targets of rejection when the number of repetitions reaches the predetermined value.

It is, therefore, possible to form an alternative of a normal brain database with higher accuracy.

-   (3) In the device for generating alternative of a normal brain     database according to the present invention, the risk a is set to a     value in the range of 0.1 to 0.6. -   (4) In the device for generating alternative of a normal brain     database according to the present invention, the risk a is set to a     value in the range of 0.2 to 0.5. -   (5) In the device for generating alternative of a normal brain     database according to the present invention, the risk a is set to a     value in the range of 0.2 to 0.4.

It is, therefore, possible to form an alternative of a normal brain database with much higher accuracy.

-   (6) In the device for generating alternative of a normal brain     database according to the present invention comprises: means for     selecting, based on normalized and anatomically standardized brain     functional image of each of the subjects, representative values from     status values of regions from the brain surface to a predetermined     vertical depth thereinto, and generating a brain surface state image     of each of the subjects using the representative values as status     values of the brain surface, wherein the regions with a status value     in the brain surface state image are used as the regions for use in     determining a brain disease.

It is, therefore, possible to form an alternative of a normal brain database for use in a determination based on brain surface state images. Brain bloodstream images or glucose metabolism images can be used as the brain functional images to generate a brain surface bloodstream image or a brain surface glucose metabolism image.

-   (7) In the device for generating alternative of a normal brain     database according to the present invention, the brain functional     images are brain bloodstream images indicating a bloodstream     associated value.

It is, therefore, possible to form an alternative of a normal brain database for use in a determination based on brain bloodstream.

-   (8) In the device for generating alternative of a normal brain     database according to the present invention, the generating means     calculates, based on brain functional images from which status     values presumed to indicate a brain disease have been rejected, mean     status value and standard deviation for each of the brain regions     for use in determining a brain disease to generate a database     indicating the normal state.

It is, therefore, possible to form an alternative of a normal brain database for use in a determination using a Z score.

-   (9) A device for generating alternative of normal brain surface     database to be used for diagnosing disease, based on brain     bloodstream images of subjects including patients which show     bloodstream associated value of each points of brain, the device     comprising:

means for normalizing bloodstream associated value at each point of each brain bloodstream image of subject based on bloodstream associated values of a part or whole of brain bloodstream image of the subject;

means for spatially transforming each brain bloodstream image of subject in accordance with anatomical standard brain;

means for generating brain surface bloodstream image of each subjects based on said anatomically transformed and normalized brain bloodstream images of subjects, said brain surface bloodstream image having surface bloodstream associated value of each surface points, said surface bloodstream associated value being selected as representative value of bloodstream associated values from brain surface to predetermined depth perpendicular to the brain surface; and

means for rejecting bloodstream associated values presumed as indicating disease with regard to each points of brain generated by brain surface bloodstream image generating means,

means for generating the alternative of normal brain database by obtaining at least mean status value for said each point of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain surface bloodstream images of subjects in which said bloodstream values presumed as indicating disease are rejected,

wherein the rejecting means calculates the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extreme studentized deviate), and rejects at least the status value selected as the ESD when the ESD exceeds a critical value λ calculated based on a predetermined risk α.

It is, therefore, possible to form an alternative of a normal brain database for use in a determination based, for example, on 3D-SSP brain surface bloodstream images.

-   (A) A device for generating alternative of normal brain database to     be used for diagnosing brain disease based on brain status images of     subjects including patients, embodying the present invention     comprises: means for normalizing status value at each point of each     brain status image of subject based on status values of a part or     whole of brain status image of the subject; means for spatially     transforming each brain status image of subject in accordance with     anatomical standard brain; means for rejecting status values     presumed as indicating disease with regard to each points of brain     to be used for diagnosing disease based on said anatomically     transformed and normalized brain status images of subjects; and     means for generating the alternative of normal brain database by     obtaining at least mean status value for said each point of brain to     be used for diagnosing disease based on said anatomically     transformed and normalized brain status images of subjects in which     said status values presumed as indicating disease are rejected. -   (B) In one embodiment of the present invention, the rejecting means     rejects extreme deviate values statistically obtained at each point     as said status values presumed as indicating disease. -   (C) In one embodiment of the present invention, the brain status     image is a brain bloodstream image which shows bloodstream     associated value -   (D) A device for generating alternative of normal brain database     embodying the present invention comprises: means for generating     brain surface status image of each subjects based on said     anatomically transformed and normalized brain status images of     subjects, said brain surface status image having surface status     value of each surface portions, said surface status value being     selected as representative value of status values from brain surface     to predetermined depth perpendicular to the brain surface; and     wherein said each surface point at which said surface status value     is indicated is used as said point of brain to be used for     diagnosing disease.

The term “normalization means” corresponds to step S13 in FIG. 7 in this embodiment.

The term “anatomical deformation means” refers to means having a function of deforming at least brain functional images of subjects from anatomical point of view, and corresponds to step S2 in FIG. 6 in this embodiment.

The term “disease data rejecting means” refers to means having a function of rejecting status values of regions presumed to be affected, and may also have a function of rejecting abnormal values that are generated by a process such as normalization. In this embodiment, it corresponds to step S36 in FIG. 21 or step S42 in FIG. 22.

The term “database generation means” refers to means having a function of generating a database necessary to determine a disease based at least on a plurality of brain functional images, and corresponds to step S37 in FIG. 21 or step S43 in FIG. 22 in this embodiment.

The term “program” is a concept including not only a program directly executable by a CPU but also a source format program, compressed program, encoded program and so on.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows plural sectional images of brain bloodstream associated value.

FIG. 2 shows images of brain surface bloodstream associated value.

FIG. 3 shows a block diagram of a device for generating alternative of normal brain database (hereinafter referred to as “alternative normal brain database”).

FIG. 4 shows a block diagram of a device for generating alternative normal brain database in another embodiment of the present invention.

FIG. 5 shows a hardware construction of the device for generating alternative normal brain database.

FIG. 6 is a flowchart of the program for generating alternative normal brain database stored in hard-disk.

FIG. 7 is a detailed flowchart for obtaining brain bloodstream data (step S1 of FIG. 6)

FIG. 8 shows normalized brain bloodstream data.

FIG. 9 shows X axis, Y axis and Z-axis relating to cephalon.

FIG. 10 shows a detailed flowchart for standardizing the bloodstream image (step S2 of FIG. 6).

FIG. 11 shows a detailed flowchart for standardizing the bloodstream image (step S2 of FIG. 6).

FIG. 12 shows a detailed flowchart for identifying the interhemispheric mid-sagittal plane of the bloodstream image (step S22 of FIG. 10).

FIG. 13 shows a detailed flowchart for identifying the interhemispheric mid-sagittal plane of the bloodstream image (step S22 of FIG. 10).

FIG. 14 shows calculation of the stochastic sign change (SSC).

FIG. 15 is a detailed flowchart for identifying the AC-PC line and the center thereof (step S23 of FIG. 10).

FIG. 16 shows steps for identifying the AC-PC line.

FIG. 17 shows estimating the landmark TH for identifying the AC-PC line.

FIG. 18 shows liner transformation of the bloodstream image to adjust the bloodstream image with the anatomical standard brain.

FIG. 19 shows non-liner transformation of the bloodstream image in accordance with the anatomical standard brain.

FIG. 20 shows a detailed flowchart for generating the brain bloodstream image (step S27 of FIG. 11).

FIG. 21 shows a detailed flowchart for generating the alternative normal brain database.

FIG. 22 shows a detailed flowchart for generating the alternative normal brain database.

FIG. 23 shows a detailed flowchart for rejecting bloodstream associated value(s) presumed as indicating disease with regard to particular voxel.

FIG. 24 shows a detailed flowchart for rejecting bloodstream associated value(s) presumed as indicating disease with regard to particular voxel.

FIG. 25 shows a table showing extracted bloodstream associated value(s).

FIG. 26 shows a table for rejecting.

FIG. 27 shows a part of the generated alternative normal brain database of brain bloodstream.

FIG. 28 shows a part of the generated alternative normal brain database of brain surface bloodstream.

FIG. 29 shows a detailed flowchart of rejecting process in another embodiment.

FIG. 30 shows a table of T for rejecting in another embodiment.

FIG. 31 shows images (brain surface extracted images), (a) mean value images and (b) standard deviation images formed by using a hypothetical image database.

FIG. 32 shows images (brain surface extracted images), (a) mean value images and (b) standard deviation images formed by using data of a hypothetical image database and additional random data which has 80% of mean value and 40% of variation coefficient.

FIG. 33 shows images (brain surface extracted images), (a) mean value images and (b) standard deviation images formed by using ESD processed (under the condition of P=0.3) data of a hypothetical image database and additional random data which has 80% of mean value and 40% of variation coefficient.

DETAILED DESCRIPTION OF EMBODIMENTS 1. Functional Block Diagram

FIG. 3 shows a functional block diagram of a device for generating alternative normal brain database of an embodiment of the present invention. The device is adapted to be able to generate the alternative normal brain database based on plural brain status images of subjects including patient(s). All of the plural brain status images provided to the device may be patient's brain status images.

Referring to FIG. 3, anatomical standardization means 2 obtains the brain status images having brain status values and spatially transforms the brain status image of each subject in accordance with anatomical standard brain such as Talairach's standard brain so that the brain status image of each subject is standardized by adjusting spatial differences.

Rejecting means 4 identifies the brain status values presumed as indicating disease based on a comparison of the brain status values of subjects with each other at each point of the standardized brain images. Then the rejecting means 4 rejects the brain status values presumed as indicating disease. This rejecting process may be carried out based on statistical method. In one embodiment, the brain status values of each point are sorted in descending order and the n-th largest and/or the m-th smallest values are rejected. The rejecting means 4 carries out the rejecting process for all of the points.

Database generating means 6 comprises mean value calculating means 8 and standard deviation calculating means 10. The mean value calculating means 8 calculates mean value of remaining brain status values after rejecting process with regard to each point. The standard deviation calculating means 10 calculates standard deviation of remaining brain status values after rejecting process with regard to each point. The calculated mean value and standard deviation is recorded associated with the information showing the position of each point. The mean values, standard deviations and positions constitute the alternative normal brain database.

FIG. 4 shows a functional block diagram of a device for generating alternative normal brain database in another embodiment of the present invention. The device generates alternative normal brain database of brain surface status for such as Three-Dimensional Stereotactic Surface Projection (3D-SSP) method.

Referring to FIG. 4, anatomical standardization means 2 obtains the brain status images having brain status values and spatially transforms the brain status image of each subject in accordance with anatomical standard brain.

Generating means 3 generates brain surface status image that has representative status value of each point of the brain surface which are selected from status values near the brain surface. The generating means 3 generates the brain surface status images of all subjects.

Rejecting means 4 rejects the brain surface status values presumed as indicating disease by comparing the brain surface status values of all subjects with each other at each point of the standardized brain status images.

Database generating means 6 comprises mean value calculating means 8 and standard deviation calculating means 10. The mean value calculating means 8 calculates mean value of remaining brain surface status values after rejecting process with regard to each point. The standard deviation calculating means 10 calculates standard deviation of remaining brain surface status values after rejecting process with regard to each point. The each calculated mean value and standard deviation is recorded at each point of the standardized brain status images. The mean values, standard deviations and positions are comprised of the alternative normal brain database.

2. Hardware Construction

FIG. 5 shows hardware construction of the device of FIGS. 3 and 4. Although the device realizes both the functions shown in FIGS. 3 and 4 in the following embodiment, the device may have either function. In the following embodiment, brain bloodstream associated value is disclosed as an example of brain status value.

CPU12 is connected to hard-disk drive 14, CD-ROM drive 16, display device 18 for displaying brain image etc., memory 20 and keyboard/mouse 22. The memory 20 is used for working area of CPU 12. Keyboard/mouse 22 is for inputting instructions from user.

The hard-disk records operating system (OS) 24 such as WINDOWS™, generating program 26 for alternative normal brain database and anatomical standard brain data 28. These programs and data are installed from recording medium such as CD-ROM 30 by using CD-ROM drive 16.

The generating program 26 fulfills its function by cooperating with the OS 24.

3. Process by the Generating Program 26 for Normal Database

FIG. 6 is a flowchart of the generating program for alternative normal brain database. The generating process comprises three steps, obtaining brain bloodstream associated values and normalizing (step S1), anatomical standardization (step S2) and generating alternative normal brain database (step S3). Although brain bloodstream associated value is used in the following embodiment, other functional image (value) may be used.

3.1 Obtaining Brain Bloodstream Associated Values

Detailed steps for obtaining and normalizing brain bloodstream associated values are shown in FIG. 7. First, CPU 12 obtains SPECT data of plural subjects including patient(s) (step S11). The device shown in FIG. 5 can obtain the SPECT data directly from SPECT device when the device is connected to SPECT device through local area network (LAN) etc. The device may obtain the SPECT data by reading the data from recording medium on which SPECT device records measured data (SPECT data). The SPECT data comprises projection data obtained from the subject by using SPECT device.

CPU 12 reconstructs three-dimensional image data which has bloodstream associated values of, for example, 2 mm cubic vowels based on the SPECT data (step S12).

Then, CPU 12 normalizes the voxel values, because the voxel value of the three-dimensional image comes under the influence of measurement condition differences including measurement device difference. In the normalizing step, CPU 12 divides the bloodstream associated value of each point, i.e. each voxel, by mean value of bloodstream associated values of entire each subject's brain (mean value of entire brain voxels) and records the divided value as normalized bloodstream associated value on the hard-disk 14.

Mean value of bloodstream associated values of subthalamic, cerebella, pons or sensorimotor cortex may be used as normalizing standard part instead of the mean value of bloodstream associated values of entire brain in normalizing step. Subthalamic, cerebella, pons or sensorimotor cortex from the SPECT data can be identified by superposing the subject's image on the anatomical standard brain image in which area of each part is predetermined. The normalizing standard part is suitably a part where reduction of bloodstream associated value is not observed in the disease to be diagnosed.

FIG. 8 shows normalized data of three-dimensional bloodstream image. In FIG. 8, column “X, Y, Z” represents position data in three-dimensional coordinate, X indicates X-coordinate data, Y indicates Y-coordinate data and Z indicates Z-coordinate data. As shown in FIG. 9, X denotes horizontal direction, Y denotes anteroposterior direction and Z denotes up and down direction of head.

The normalized bloodstream data as shown in FIG. 8 are generated for each subject and recorded on hard-disk 14.

3.2 Anatomical Standardization

CPU 12 spatially transforms normalized three-dimensional bloodstream image of each subject in accordance with anatomically standard brain (step S2 of FIG. 6).

Detailed flowchart of anatomical standardization is shown in FIGS. 10 and 11. CPU 12 reads out the normalized 3-D bloodstream image (see FIG. 8) of initial subject from the hard-disk 14 (step S21).

3.2.1 Identifying the Interhemispheric Mid-Sagittal Plane

Then, CPU 12 identifies the interhemispheric mid-sagittal plane of the normalized 3-D bloodstream image read out in step S21 (step S22). Referring to FIG. 9, the interhemispheric mid-sagittal plane is defined as Y-Z plane including line A which passes center of a head in a horizontal direction: that is, a plane which equally divides the head with regard to horizontal direction.

In this embodiment, briefly, method for identifying the interhemispheric mid-sagittal plane is as follows:

First, center point of the normalized 3-D bloodstream image is identified and a Y-Z plane including the center point is assumed as the interhemispheric mid-sagittal plane. Then, the normalized 3-D bloodstream image is flipped with respect to the assumed interhemispheric mid-sagittal plane as flipping plane. An image which is symmetric to the assumed interhemispheric mid-sagittal plane is generated. Similarity index between the generated plane symmetry image and original image (the normalized 3-D bloodstream image) is calculated. Then, the assumed interhemispheric mid-sagittal plane is moved along the X direction, rotated around the Z axis and rotated around the Y axis. The similarity index is calculated for each assumed interhemispheric mid-sagittal plane.

The interhemispheric mid-sagittal plane is identified by selecting the assumed interhemispheric mid-sagittal plane which has maximum similarity index, because the similarity index should be maximum when the normalized 3-D bloodstream image is flipped at center plane.

Detailed flowchart for identifying the interhemispheric mid-sagittal plane is shown in FIGS. 12 and 13. CPU 12 decides center point x₀, y₀, z₀ of the normalized 3-D bloodstream image by simply using coordinate position. New center point is determined by moving the center point along X direction with Δx (step S222).

CPU 12 assumes that original point of coordinate is as the new center point and rotates the Y-Z plane which passes through the center point around the Z axis with ΔΦz and around the Y axis with ΔΦy. CPU 12 assumes the moved and rotated plane as the interhemispheric mid-sagittal plane (step S225)

CPU 12 generates flipped normalized 3-D bloodstream image which is symmetric to the assumed interhemispheric mid-sagittal plane by flipping the normalized 3-D bloodstream image (step S226). Then, CPU 12 calculates similarity index between the original and the flipped normalized 3-D bloodstream images (step S227). In this embodiment, Stochastic Sign Change (SSC) is calculated as similarity index.

Concept of Calculating SSC is as Follows:

If the two images have higher similarity, the corresponding pixel values between the two images have small difference. Therefore, if the two images have higher similarity, the signs of the differences along the pixels change randomly. In the SSC method, number of sign changes along the pixels is calculated as SSC value. If the SSC value is large, it is judged that the similarity of two images is high.

SSC is calculated by summation of SSC_(x), SSC_(y) and SSC_(z) in this embodiment. Referring to FIG. 14, planes ZX₁, ZX₂, . . . ZX_(N) and planes YX₁, YX₂, . . . YX_(N) are assumed for calculating SSC_(x). For each plane, SSC is calculated by scanning the plane. SSC_(x) is calculated by summation of SSC of all planes ZX₁, ZX₂, . . . ZX_(N) and YX₁, YX₂, . . . YX_(N). CPU 12 also calculates SSC_(y) by summation of SSC of planes ZY₁, ZY₂, . . . ZY_(N) and planes YX₁, YX₂, . . . YX_(N) and SSC_(z) by summation of SSC of planes ZY₁, ZY₂, . . . ZY_(N) and planes ZX₁, ZX₂, . . . ZX_(N). Then, CPU 12 obtains SSC(Δx, ΔΦz, ΔΦy) with regard to the assumed interhemispheric mid-sagittal plane as summation of SSG_(x), SSC_(y) and SSC_(z) and stores SSC(Δx, ΔΦz, ΔΦy) on the memory 20.

CPU 12 changes Δx from −I to I at 1 voxel step to move the assumed interhemispheric mid-sagittal plane along X axis and changes ΔΦz and ΔΦy from −ΦI to ΦI to rotate the assumed interhemispheric mid-sagittal plane. CPU 12 calculates SSC(Δx, ΔΦz, ΔΦy) for all assumed interhemispheric mid-sagittal planes which are determined by all combinations of Δx, ΔΦz and ΔΦy (step S221, S223 and S224).

After calculating SSC(Δx, ΔΦz, ΔΦy) of all the assumed interhemispheric mid-sagittal planes, CPU 12 identifies the interhemispheric mid-sagittal plane which has the largest SSC(Δx, ΔΦz, ΔΦy) among all the assumed interhemispheric mid-sagittal planes (step S229). Identifying the interhemispheric mid-sagittal plane is disclosed in Minoshima et al., “An Automated Method for Rotational Correction and Centering of Three-Dimensional Functional Brain Images” J Nucl Med 1992; 33: 1579-1585, which is expressly incorporated by reference herein.

3.2.2 Identifying AC-PC Line and the Center Thereof

After identifying the interhemispheric mid-sagittal plane, CPU 12 identifies line which passes through the anterior and the posterior commissures of the brain (herein after referred to as “AC-PC line”) and the center thereof (step S23 of FIG. 10). In this embodiment, 4 landmarks, occipital pole point (OP), the subthalamic point (TH), the inferior aspect of the anterior corps callosum (CC) and the frontal pole point (FP) of the brain are decided and then AC-PC line is identified as straight line connecting the 4 landmarks.

Detailed flowchart for identifying AC-PC line and center thereof is shown in FIG. 15. CPU 12 reads out sectional bloodstream image at the interhemispheric mid-sagittal plane from the hard-disk 14. CPU 12 further reads out sectional bloodstream images of planes which are parallel to and near the interhemispheric mid-sagittal plane (step S231). FIG. 16A shows an example of the sectional bloodstream image at the interhemispheric mid-sagittal plane.

CPU 12 detects outer border of brain and border between white matter and gray matter of brain for each normalized 3-D bloodstream image (step S232). The detection can be carried out by detecting the border between the area of bloodstream existing and the area of bloodstream non-existing. FIG. 16B shows detected border image of the interhemispheric mid-sagittal plane for example.

Then, CPU 12 identifies the occipital pole point (OP) as the most posterior point of the border image of the interhemispheric mid-sagittal plane (see FIG. 16B). CPU 12 also identifies the occipital pole points (OPs) for the other border images of the other sectional bloodstream images. CPU 12 calculates mean of y coordinate values and mean of z coordinate values of all the identified occipital pole points (OPs). CPU 12 decides the position of the occipital pole point (OP) based on the mean of y coordinates values and mean of z coordinate values on the interhemispheric mid-sagittal plane (step S233).

Next, CPU 12 finds out U shaped border area as shown in FIG. 16C. The inferior aspect of the anterior corps callosum (CC) is decided by detecting contact point of U shaped border and tangent line from the occipital pole point (OP) to the U shaped border based on the border image. CPU 12 decides the inferior aspect of the anterior corps callosum (CC) for each border image of the sectional bloodstream image. CPU 12 calculates mean of y coordinate values and mean of z coordinate values of all the found inferior aspect of the anterior corps callosum (CC). CPU 12 decides the position of the inferior aspect of the anterior corps callosum (CC) based on the mean of y coordinate values and mean of z coordinate values on the interhemispheric mid-sagittal plane (step S234).

CPU 12 identifies the frontal pole point (FP) as the most anterior point of the border image of the interhemispheric mid-sagittal plane (see FIG. 16B). CPU 12 also identifies the frontal pole points (FPs) based on the other border images of the other sectional bloodstream images. CPU 12 calculates mean of y coordinate values and mean of z coordinate values of all the identified frontal pole points (FPs). CPU 12 decides the position of the frontal pole point (FP) based on the mean of y coordinate values and mean of z coordinate values on the interhemispheric mid-sagittal plane (step S235).

CPU 12 decides the subthalamic point (TH) (step S236). To decide the subthalamic point (TH), CPU 12 finds out the thalamic center at first. The thalamic center can be estimated as the point having local maximum bloodstream associated value and near the AC-PC line. In this embodiment, CPU 12 finds out a point having the local maximum bloodstream associated value within the circle of radius r on the sectional bloodstream image and identifies the point as the thalamic center, where center of the circle is a center point of line connecting the occipital pole point (OP) and the frontal pole point (FP) and the radius r is tenth of the length between OP and FP.

After deciding the thalamic center, CPU 12 plots an imaginary circle having predetermined radius r on the sectional bloodstream image. As shown in FIG. 17A, CPU 12 makes imaginary point P1 where the imaginary circle and tangent line from OP to the imaginary circle contact and imaginary point P2 which is diametrically opposite to P1 on the imaginary circle. CPU 12 stores bloodstream associated value on each imaginary point P2 of the imaginary circle on the memory 20 when the radius r of the imaginary circle is varied from the length of 1 pixel to 12 pixels by 0.5 pixel step. FIG. 17B shows a graph plotting the bloodstream associated value on the imaginary point P2 at each radius r. In this graph, horizontal axis denotes the radius r and r=0 is positioned at the center. To the left and right sides from the center, r is increased. The graph shows bloodstream associated value becomes maximum when the radius r is 0. Increasing r makes bloodstream associated value on the imaginary point P2 reduced toward minimum bloodstream associated value which corresponds to lateral ventricular (LV).

CPU 12 decides radius r which corresponds border of thalamus at which the bloodstream associated value on the imaginary point P2 shows 70% of the maximum bloodstream associated value where the minimum bloodstream associated value is set as 0% (see FIG. 17). Then, CPU 12 identifies the imaginary point P1 on the imaginary circle having the decided radius r as the subthalamic point (TH). CPU 12 also decides the subthalamic point (TH) based on the other sectional bloodstream images. CPU 12 calculates mean of y coordinate values and mean of z coordinate values of all the decided subthalamic points (THs). CPU 12 decides the position of the subthalamic point (TH) based on the mean of y coordinate values and mean of z coordinate values on the interhemispheric mid-sagittal plane (see FIG. 16B).

CPU 12 generates regression line connecting through the occipital pole point (OP), the subthalamic point (TH), the inferior aspect of the anterior corps callosum (CC) and the frontal pole point (FP) on the interhemispheric mid-sagittal plane. CPU 12 identifies the generated regression line as the AC-PC line a (step S237). Then, center point 8 of the AC-PC line a is decided as a point which possesses half length of the AC-PC line as shown in FIG. 16D (step S238). Identifying the AC-PC line and the center thereof is disclosed in Minoshima et al., “An Automated Detection of the Intercommissural Line for Stereotactic Localization of Functional Brain Images” J Nucl Med 1993; 34: 322-329, which is expressly incorporated herein by reference.

3.2.3 Positioning Subject's Image to Normal Brain

Once the AC-PC line and the center thereof are identified, CPU 12 positions the normalized 3-D bloodstream image of each subject to the standard brain image data 28 recorded in the hard-disk 14 by superposing the AC-PC lines and the centers of both images (step S24 of FIG. 10). Direction of subject's normalized 3-D bloodstream image is aligned to that of the standard brain image by matching the AC-PC line of the subject's normalized 3-D bloodstream image to that of the standard brain image. Position of subject's normalized 3-D bloodstream image is aligned to that of the standard brain image in the anteroposterior direction by matching the center of the AC-PC line to that of the standard brain image.

In this embodiment, ¹⁸F-FDG PET image is used as the standard brain image. The standard image is made by transforming the images of a number of normal subjects in accordance with the standard brain shape and averaging them. The standard brain image of ¹⁸F-FDG PET is well-used and easily obtainable.

3.2.4 Linearly Transforming Subject's Image

After positioning the subject's normalized 3-D bloodstream image to the normal brain, CPU 12 linearly transforms the subject's normalized 3-D bloodstream image in accordance with the standard brain image by the following steps.

First, CPU 12 provides Y-axis which is the AC-PC line of the subject's normalized 3-D bloodstream image, Z-axis which is the line on the interhemispheric mid-sagittal plane which passes through the center of the AC-PC line and is perpendicular to the AC-PC line and X-axis which is the normal line of the interhemispheric mid-sagittal plane which passes through the center of the AC-PC line. The center 8 of the AC-PC line is, therefore, provided as the original point of the coordinate system. Then, CPU 12 obtains Y coordinate positions of the most anterior (FP) and the posterior (OP) points, Z coordinate positions of the most upper (point T shown in FIG. 18) and the lowest (point B shown in FIG. 18) points and X coordinate positions of the right most and the left most points of the subject's normalized 3-D bloodstream image. CPU 12 also obtains Y coordinate positions of the most anterior (point FP′ shown in FIG. 18) and the posterior (point OP′ shown in FIG. 18) points, Z coordinate positions of the most upper (point T′ shown in FIG. 18) and the lowest (point B′ shown in FIG. 18) points and X coordinate positions of the right most and the left most points of the standard brain image. The Y coordinate positions, the z coordinate positions and the X coordinate positions of the standard brain image may be previously obtained and stored.

CPU 12 transforms the subject's normalized 3-D bloodstream image so that the outer border of the subject's bloodstream image is adjusted to the outer border of the standard brain image by using the obtained X, Y and Z coordinate positions of the subject's normalized 3-D bloodstream image and shoes of the standard brain image (step S25 of FIG. 10).

Referring to FIG. 18, for example, the subject's normalized 3-D bloodstream image (shown by solid line) is superposed on the standard brain image (shown by chained line). CPU 12 finds the most upper point T of the outer border of the subject's normalized 3-D bloodstream image and obtains Z coordinate value Lz of the point T. CPU 12 also finds the most upper point T′ of the outer border of the standard brain image and obtains Z coordinate value L′z of the point T′. Then, CPU 12 transforms Z coordinate values of the subject's normalized 3-D bloodstream image by the following equation:

Z′=Z(Lz/L′z)

where Z′ is the transformed Z coordinate value of the subject's normalized 3-D bloodstream image, Z is the original Z coordinate value of the subject's normalized 3-D bloodstream image, Lz is the Z coordinate value of the point T and L′z is the Z coordinate value of the point T′.

By the transformation, the subject's normalized 3-D bloodstream image is adjusted to the standard brain image with respect to upper direction.

CPU 12 also transforms the subject's normalized 3-D bloodstream image with respect to lower, right, left, anterior and posterior direction by the same way as that of the upper direction.

3.2.5 Non-Linearly Transforming Subject's Image

Although the subject's normalized 3-D bloodstream image is aligned in accordance with the standard brain image by the liner transforming, some misalignments between the both images still remain. CPU 12 partially transforms the subject's normalized 3-D bloodstream image based on profile curve of the bloodstream associated value of the subject's normalized 3-D bloodstream image and profile curve of the glucose metabolism of the standard brain image (step S26).

Referring to FIG. 19A, plural landmarks C1, C2 . . . Cn on white matter are predetermined in the standard brain image. Plural landmarks S1, S2 . . . Sn on brain surface are also predetermined corresponding to the landmarks C1, C2 . . . Cn in the standard brain image. In FIG. 19A, the landmarks S1, S2 and S3 of brain surface are provided corresponding to the landmarks C1 of white matter. CPU 12 generates the profile curve of the bloodstream associated values by obtaining the bloodstream associated values on the subject's normalized 3-D bloodstream image along a line L1 connecting the landmarks from C1 to S1 (see solid curve of FIG. 19B). CPU 12 obtains the profile curve of glucose metabolism on the standard brain image along the line L1 (see chained curve of FIG. 19B). In this embodiment, the profile curve of glucose metabolism on the standard brain image is prerecorded in the hard-disk 14.

CPU 12 partially transforms the subject's normalized 3-D bloodstream image so that the profile curve of the bloodstream associated values of the subject's normalized 3-D bloodstream image accords to the profile curve of the glucose metabolism values at each corresponding point, because the profile curve of the glucose metabolism values is well matched with that of the bloodstream associated values. The partial transformation (non-liner transformation) is carried out along the direction of line L1 and the landmark C1 is fixed. With regard to the line L2 between the landmarks C1 and S2 and the line L3 between the landmarks C1 and S3, CPU 12 also transforms the subject's normalized 3-D bloodstream image by same way. In area between the lines L1 and L2 (L2 and L3), CPU 12 transforms these areas based on the transforming rates on L1 and L2 (L2 and L3). The non-liner transformation is carried out with regard to each line L connecting each landmark C of white matter and corresponding landmark S of brain surface. Although in the above description, non-linear transformation is described on two-dimensional model, the non-linear transformation is carried out in three-dimensional space in this embodiment.

The liner and non-liner transforming is disclosed in Minoshima et. al. “An Anatomic Standardization: Liner Scaling and Nonliner Warping of Functional Brain Images” J Nucl Med 1994; 35: 1528-1537, which is expressly incorporated by reference herein.

3.2.6 Generating Brain Surface Bloodstream Image

After anatomically transforming the subject's transformed 3-D bloodstream image, CPU 12 generates the brain surface bloodstream image (step S27 of FIG. 11). FIG. 20 shows detailed flowchart for generating the brain surface bloodstream image.

CPU 12 identifies each brain surface voxel positioned at the most outer surface of the subject's normalized 3-D bloodstream image and assigns identification number for each identified surface voxel (step S271). In this embodiment, approximate 20,000 surface voxels are identified.

CPU 12 carries out the following steps for each identified surface voxel:

CPU 12 obtains bloodstream associated values of several number (“6” in this embodiment) of voxels from the brain surface to predetermined depth perpendicular to the brain surface in the subject's transformed 3-D bloodstream image (step S273). Then, CPU 12 selects the maximum bloodstream associated value among the several numbers of voxels as representative bloodstream associated value and the selected maximum bloodstream associated value is assigned to the brain surface voxel (step S274).

CPU 12 carries out such process for each brain surface voxel. Then, brain surface bloodstream image in which the representative bloodstream associated value is indicated at each surface voxel is obtained.

CPU 12 stores the brain bloodstream images generated in step S26 and the brain surface bloodstream images generated in step S27 on the hard-disk 14 (step S28 of FIG. 11).

When next subject's unprocessed bloodstream image exists, CPU 12 reads out the next subject's unprocessed bloodstream image from the hard-disk 14 (step S30) and carries out the process described above (following steps of step S22 of FIG. 10). When all subject's bloodstream images are processed (step S29), CPU 12 finishes the anatomical standardization (step S2 of FIG. 6).

3.3 Generation of Alternative of Normal Brain Database

When anatomically standardized brain bloodstream image and brain surface bloodstream image of each subject are generated as described above, the CPU 12 next generates an alternative of a normal brain database (step S3 in FIG. 6). When the subjects are all healthy subjects, a normal brain database can be generated by obtaining the mean value and standard deviation for each region of the standardized brain bloodstream images and brain surface bloodstream images of the subjects. In this embodiment, however, some or all of the subjects are patients. It is, therefore, necessary to exclude bloodstream associated values of the regions determined as being affected before generating an alternative of a normal brain database.

A process for generating an alternative of a normal brain database is shown in FIG. 21 and FIG. 22. The CPU 12 extracts a brain bloodstream associated value in a certain voxel (target voxel) of the brain bloodstream images of all the subjects (step S34). FIG. 25 shows the extracted bloodstream associated values. In this drawing, the bloodstream associated values of the subjects in a voxel at a position of x=11, y=25 and z=135 is shown. The patient ID is an identifier assigned to each subject. It should be noted that the bloodstream associated values are normalized.

Next, the CPU 12 rejects outliers (step S36). In this example, outliers are rejected by GESD (Generarized extream studentized deviate) testing. A detailed flowchart of the testing procedure is shown in FIG. 23 and FIG. 24.

First, the CPU 12 sets the maximum rejection number TN to a predetermined value using the number n of subjects (i.e., the number of data of bloodstream associated value in the target voxel) and a repetition coefficient i of 0 (step S361). The maximum rejection number TN is the maximum number of data that can be rejected and may be set to an arbitrary value. In this embodiment, a value obtained by rounding n/2 down to an integer is used as TN.

The CPU 12 calculates the arithmetic mean (step S362) and standard deviation (step S363) of the bloodstream associated values in the target voxel except those already deviated. At this point, because there is no deviated value yet, the arithmetic mean MEAN and standard deviation SD of bloodstream associated values I₁, I₂ . . . I_(n) of all the subjects are calculated.

Then, the CPU 12 calculates deviations D₁, D₂ . . . D_(n) of each subject from MEAN, and calculates R₁, R₂ . . . R_(n) for each subject based on the following equation.

R=Deviation D/standard deviation SD

The CPU 12 selects the maximum value from the calculated values R₁, R₂ . . . R_(n) of the subjects and defines it as R_(max1) (step S364). The value R_(max1) is referred to as “ESD (extreme studentized deviate).” The CPU 12 stores the ID and R of the subject having R selected as R_(max1) as deviated data in the memory 20 (only the subject ID may be stored).

The CPU 12 next calculates a probability P based on the following equation (step S365).

P=1−(α/(2(n−i+1))

where α represents a risk, which is an indication to determine the extent of rejection. The risk a takes a value between 0 and 1. The greater the value, the greater the extent of rejection. Therefore, the greater the risk α is, the more reliably outliers can be rejected but the higher the possibility of rejecting normal values accidentally is. On the contrary, the smaller the risk a, the lower the possibility of rejecting normal values accidentally but the higher the possibility of failing to reject outliers.

As the value of risk a, a value preliminarily set to an adequate value based on the target disease and the type of functional images (and stored in the hard disk 14) is used. When a normal database for use in a determination of a brain disease is generated based on SPECT image data, the risk α is preferably in the range of 0.1 to 0.6, more preferably in the range of 0.2 to 0.5, and especially preferably in the range of 0.2 to 0.4 (refer to Examples described later).

Here, because i=0, the probability P is calculated as P=1−(α/(2(n+1)).

Next, the CPU 12 obtains the t value (T) for the degree of freedom of n−i+1 corresponding to the probability P from a T-distribution table (step S366). Here, because i=0, a value T in a t-distribution table of the degree of freedom of n+1 is acquired. In addition, the CPU 12 calculates a critical value λ using the following equation (step S367).

A=(n−i)T

B=SQRT((n−i−1+T ²)(n−i−1))

λ=A/B

wherein SQRT( ) represents the square root of the value enclosed within the parenthesis.

The CPU 12 next determines whether or not R_(max1) as the ESD calculated in step S364 exceeds the critical value λ (step S368). When R_(max1) exceeds the critical value λ, a flag to indicate that the value should be rejected is stored for the deviated data R_(max1) stored in the memory 20 (step S369). For example, when R₈ is selected as R_(max1) in step S364 and when R₈ exceeds the critical value λ, a rejection flag is stored as shown in the first line of FIG. 26. When R_(max1) is equal to or smaller than the critical value λ, no rejection flag is stored.

Then, the CPU 12 increments i (step S370), and executes step S362 and the following steps. Here, step S362 and the following steps are executed with i=1.

In steps S362 and S363, the arithmetic mean MEAN and standard deviation SD are calculated without the deviated data stored in the memory 20. In step S364, R_(max2) is calculated. Then, a critical value λ is calculated with i=1 (steps S365, S366 and S367).

The CPU 12 determines whether or not R_(max2) exceeds the critical value λ (step S368), and, when R_(max2) exceeds the critical value λ, an exclusion flag is stored for the deviated data R_(max2) in the memory 20 (step S369).

Further, the CPU 12 increments i (step S370), and executes step S362 and the following steps. The CPU 12 repeats this procedure until i reaches the maximum rejection number TN (step S371).

As a result of the above process, a deviated data table as shown in FIG. 26 is stored in the memory 20.

When i reaches the maximum rejection number TN, the CPU 12 finds the data for which a rejection flag was stored last from the deviated table. In other words, the CPU 12 specifies the data having the largest i among the data for which a rejection flag was stored. In the case of the deviated table shown in FIG. 26, the data with patient ID=15 are the data for which a rejection flag was stored last. Then, the CPU 12 determines all the data for which a rejection flag was stored before the data for which a rejection flag was stored last (data having a smaller i) as targets of rejection (step S372). Although a rejection flag is not attached to the data with patient ID=22 in FIG. 26, the data is determined as a target of rejection because it precedes (has a smaller i than) the data with patient ID=15. In the example of FIG. 26, the bloodstream associated values with patient ID 8, 5, 11 and 15 are rejected.

By performing rejection by GESD rejection testing as described above, bloodstream associated values presumed to indicate a brain disease can be rejected. In addition, outliers due to an influence of normalization and so on can be also rejected.

Next, the CPU 12 calculates and stores the mean value and standard deviation of the bloodstream associated values left unrejected (step S37 in FIG. 21). In this way, the mean value and standard deviation of the bloodstream associated values in the voxel (11, 25 and 135) are calculated and stored (refer to FIG. 24).

The CPU 12 calculates and stores the mean value and standard deviation for all the voxels as described above (steps S31, S32, S33 and S38). In this way, an alternative of a normal brain database based on brain bloodstream images as shown in FIG. 24 is generated.

The CPU 12 next calculates the mean value and standard deviation for each brain surface voxel of the brain surface bloodstream images in the same manner. The CPU 12 extracts a brain bloodstream associated value in a certain brain surface voxel from the brain surface bloodstream images of all the subjects (step S40).

Then, the CPU 12 executes the above-described GESD testing (FIG. 23 and FIG. 24) to reject outliers (step S42).

Next, the CPU 12 calculates and stores the mean value and standard deviation of the bloodstream associated values left unrejected (step S43). In this way, the mean value and standard deviation of the bloodstream associated values in the brain surface voxel are calculated and stored (refer to FIG. 28).

The CPU 12 calculates and stores the mean value and standard deviation for all the brain surface voxels as described above (steps S39 and S44). In this way, an alternative of a normal brain database based on brain surface bloodstream images as shown in FIG. 28 is generated.

4 Other Embodiments

Identifying interhemispheric mid-sagittal plane and PC-AC line and positioning AC-PC line shown as steps S22, S23 and S24 of FIG. 10 may be substituted by aligning method using mutual information in which the subject's bloodstream image is moved against the standard brain image in dribs and mutual information between the both images is used to find the best aligning position. The aligning method using mutual information is disclosed in F. Maes et al., “Multimodality Image Registration by Maximization of Mutual Information,” IEEE Transactions on Medical Imaging, (USA), 1997, 16, 2, p 187-198, which is expressly incorporated herein by reference. The aligning method using mutual information can be achieved by the stereo program contained in 3D-SSP program (provided by Satoshi Minoshima, Professor of University of Washington).

Although the bloodstream associated values regarded as outlier due to the normalization are rejected in addition to the bloodstream associated values regarded as brain disease part in the above mentioned embodiment. However, only one of the bloodstream associated values regarded as outlier due to the normalization or the bloodstream associated values regarded as brain disease part may be rejected.

Instead of the above mentioned rejection method, following statistical rejection methods may be used. For example, THOMPSON's method (William R. Thompson, The annals of Mathematical Statistics, Vol 6, No. 4 (December 1935), pp 214-219), SMIRNOV-GRUBBS's method (Frank E. Grubbs, The annals of Mathematical Statistics, Vol 21, No. 1 (March 1950), pp. 27-58) may be used.

When using THOMPSON's method, step S36 of FIG. 21 and step S42 of FIG. 22 are substituted for steps shown in FIG. 29.

CPU 12 sets a bloodstream associated value Ip (x, y, z) of a subject as target subject's bloodstream associated value (step S352). CPU 12 calculates mean value I_(mean) (x, y, z) and standard deviation SD (x, y, z) of bloodstream associated values of all subjects (step S353). Then, CPU 12 calculates the absolute value of the subtract mean value I_(mean) from the bloodstream associated value Ip of the target and divides the absolute value by standard deviation SD to obtain index T₀.

CPU 12 compares the index T₀ and limit Tα (step S355). The limit Tα is decided by combination of risk rate a and the number of subjects M. In this embodiment, CPU 12 obtains the limit Tα according to a table (per-recorded on the hard-disk 14) as shown in FIG. 30. The risk rate α is a factor indicating rate of rejection and more bloodstream associated values are rejected when the risk rate α grows higher. When the number M of subject is 20 and the risk rate α is 5%, the limit Tα is decided as 1.93 from the table of FIG. 30.

CPU 12 rejects the target subject's bloodstream associated value Ip (x, y, z) when the index T₀ is larger than the limit Tα (step S35) and does not reject the target subject's bloodstream associated value Ip (x, y, z) when the index T₀ is not larger than the limit Tα.

CPU 12 repeatedly carries out the above mentioned process for each subject's bloodstream associated value as the target bloodstream associated value (steps S351 and S357).

In the embodiment shown in FIG. 29, the same risk rate α is used for both the upper side and the lower side outlier. However, the risk rate α of one side on which the disease affects the bloodstream (for example, lower bloodstream associated value side) may be higher than that of the other side on which the disease does not affect the bloodstream (for example, higher bloodstream associated value side). Instead, the rejection process may be carried out only for the one side in which the disease affects the bloodstream.

As shown in the table of FIG. 30, the number of subjects should be more than 3 for rejecting process. In order to secure the degrees of freedom, it is necessary to have predetermined number of subjects for the statistical rejecting process such as Thompson's method.

Although in the above mentioned embodiments, brain bloodstream image obtained by SPECT and PET etc. is used, other functional images may be used.

Although in the above mentioned embodiments, normalization of status values (bloodstream associated values) is carried out before the spatial transformation, the normalization may be carried out after the spatial transformation or the normalization and the spatial transformation may be carried out simultaneously.

EXAMPLE

The method for generating an alternative of a normal brain database according to the present invention was verified using a healthy subject normal database generated using a plurality of head SPECT images.

As original data, surface pixel data from a normal database, ChibaDB_ver2IMP60-64AGLBMNSFM (provided by University Corporation Chiba University), generated based on head SPECT data of healthy subjects obtained after administration of IMP (which are hereinafter referred to as “original image data”) were used. Random data following a normal distribution with a coefficient of variation of 10% with respect to the mean value in each pixel were calculated for each pixel of original image data, and a virtual image database including 40 sets of virtual image data (images are shown in FIG. 31, and the database is hereinafter referred to simply as “virtual image database”) was created. Ten pieces of random data generated using the mean value and coefficient of variation shown in Tables 1 to 4 were mixed into the data set of each pixel in the virtual image database to form a data group (corresponding to 50 sets of image data) for verification.

Abnormal values were excluded from the data group of each pixel by

GESD testing (refer to the afore mentioned embodiment) with a risk α shown in Tables 1 to 4 to generate α database. It should be noted that the number of targets in the GESD testing was 25. A t-test (with a significance level of 5%) and an F-test were conducted between the obtained mean value and standard deviation of each pixel in the database and the corresponding pixel in the virtual image database to obtain the number of pixels having significantly different standard deviation and mean value (Examples 1 to 28).

In addition, a t-test (with a significance level of 5%) and an F-test were conducted between the above data group of each pixel for verification without GESD testing and the data in the virtual image database to check for each pixel whether there was a significant difference from the corresponding pixel in the virtual image database (Comparative Examples 1 to 4).

TABLE 1 Conditions for generation of database in each Examples and Comparative Examples Random data mixed into original image data Coefficient of α value in Mean value variation % ESD testing Comparative 80% of mean value 30% — example 1 of original image Example 1 data 0.05 Example 2 0.10 Example 3 0.20 Example 4 0.30 Example 5 0.40 Example 6 0.50 Example 7 0.60

TABLE 2 Conditions for generation of database in each Examples and Comparative Examples Random data mixed into original image data Coefficient of α value in Mean value variation % ESD testing Comparative 80% of mean value 40% — example 2 of original image Example 8 data 0.05 Example 9 0.10 Example 10 0.20 Example 11 0.30 Example 12 0.40 Example 13 0.50 Example 14 0.60

TABLE 3 Conditions for generation of database in each Examples and Comparative Examples Random data mixed into original image data Coefficient of α value in Mean value variation % ESD testing Comparative 120% of mean 30% — example 3 value of original Example 15 image data 0.05 Example 16 0.10 Example 17 0.20 Example 18 0.30 Example 19 0.40 Example 20 0.50 Example 21 0.60

TABLE 4 Conditions for generation of database in each Examples and Comparative Examples Random data mixed into original image data Coefficient of α value in Mean value variation % ESD testing Comparative 120% of mean 30% — example 4 value of original Example 22 image data 0.05 Example 23 0.10 Example 24 0.20 Example 25 0.30 Example 26 0.40 Example 27 0.50 Example 28 0.60 The results are summarized in Tables 5 to 8.

In the case of the databases generated without exclusion by GESD testing, it was determined that 80% or more of all the pixels had a significant difference in standard deviation from the corresponding pixels in the virtual image database, and 10% or more of all the pixels had a significant difference in mean value from the corresponding pixels in the virtual image database (Comparative Examples 1 to 4). As one example, images created using the data of Comparative Example 2 are shown in FIG. 32.

In the databases generated after the exclusion by GESD testing, on the other hand, the pixels determined to have a significant difference in standard deviation from the corresponding pixels in the virtual image database and the pixels determined to have a significant difference in mean value from the corresponding pixels in the virtual image database were both significantly decreased to approximately 30% or less and approximately 4% or less, respectively. The results indicate that a favorable normal database can be generated by the method according to the present invention. As one example, images created using the data of Example 11 are shown in FIG. 33. As can be apparent from this drawing, when exclusion by GESD testing is performed, the created images can be visually close to the images created using the virtual image database (FIG. 31).

In addition, the number of pixels determined to have a significant difference in standard deviation and mean value from the corresponding pixels in the virtual image database varied depending on the risk α in the GESD testing. In other words, the number of pixels determined to have a significant difference in standard deviation and mean value from the corresponding pixels in the virtual image database was smaller when the risk a was in the range of 0.1 to 0.6, much smaller when the risk α was in the range of 0.2 to 0.5, and especially smaller when the risk α was in the range of 0.2 to 0.4. This result proves that the risk α in the GESD testing is preferably in the range of 0.1 to 0.6, more preferably in the range of 0.2 to 0.5, especially preferably in the range of 0.2 to 0.4.

TABLE 5 Results of determination of significant difference between generated database and virtual image database Percentage % of pixels determined to have significant difference from corresponding pixels in virtual image database α value in Mean value Standard deviation ESD testing Comparative 13.6 84.6 — example 1 Example 1 3.6 33.7 0.05 Example 2 2.0 20.8 0.10 Example 3 1.1 10.2 0.20 Example 4 1.1 7.6 0.30 Example 5 1.6 9.0 0.40 Example 6 2.8 13.1 0.50 Example 7 4.2 19.2 0.60

TABLE 6 Results of determination of significant difference between generated database and virtual image database Percentage % of pixels determined to have significant difference from corresponding pixels in virtual image database α value in Mean value Standard deviation ESD testing Comparative 12.3 94.7 — example 2 Example 8 1.5 29.2 0.05 Example 9 0.8 16.5 0.10 Example 10 0.5 7.4 0.20 Example 11 0.7 6.0 0.30 Example 12 1.3 8.3 0.40 Example 13 2.5 13.0 0.50 Example 14 4.1 19.4 0.60

TABLE 7 Results of determination of significant difference between generated database and virtual image database Percentage % of pixels determined to have significant difference from corresponding pixels in virtual image database α value in Mean value Standard deviation ESD testing Comparative 11.1 97.0 — example 3 Example 15 1.1 27.9 0.05 Example 16 0.5 15.2 0.10 Example 17 0.3 6.6 0.20 Example 18 0.7 5.9 0.30 Example 19 1.4 8.5 0.40 Example 20 2.5 13.4 0.50 Example 21 4.0 19.9 0.60

TABLE 8 Results of determination of significant difference between generated database and virtual image database Percentage % of pixels determined to have significant difference from corresponding pixels in virtual image database α value in Mean value Standard deviation ESD testing Comparative 9.7 99.3 — example 4 Example 22 0.4 20.7 0.05 Example 23 0.1 10.2 0.10 Example 24 0.2 4.5 0.20 Example 25 0.6 5.2 0.30 Example 26 1.3 8.4 0.40 Example 27 2.4 13.6 0.50 Example 28 4.0 20.3 0.60 

1. A device for generating alternative of normal brain database to be used for diagnosing brain disease based on brain status images of subjects including patients, the alternative of normal brain database showing normal status of each points of brain, the device comprising: means for normalizing status value at each point of each brain status image of subject based on status values of a part or whole of brain status image of the subject; means for spatially transforming each brain status image of subject in accordance with anatomical standard brain; means for rejecting status values presumed as indicating disease with regard to each points of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain status images of subjects; and means for generating the alternative of normal brain database by obtaining at least mean status value for said each point of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain status images of subjects in which said status values presumed as indicating disease are rejected, wherein the rejecting means calculates the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extream studentized deviate), and rejects at least the status value selected as the ESD when the ESD exceeds a critical value λ calculated based on a predetermined risk α.
 2. The device for generating alternative of a normal brain database according to claim 1, wherein the rejecting means, for each points, i) forms a target status value group consisting of the status values of all the plurality of subjects, ii) calculates the ESD of the target status value group and stores the ESD as a deviated status value, iii) determines whether or not the ESD exceeds a critical value λ and stores the result of determination in a correlated manner with the deviated status value, iv) rejects the deviated status value from the target status value group to form a new target status value group and performs the steps ii) to iv) on the new target status value group when the number of repetitions has not reached a predetermined value, and v) determines all of the deviated status values determined before the ESD exceeds the critical value λ as targets of rejection when the number of repetitions reaches the predetermined value.
 3. The device for generating alternative of a normal brain database according to claim 1, wherein the risk α is set to a value in the range of 0.1 to 0.6.
 4. The device for generating alternative of a normal brain database according to claim 3, wherein the risk α is set to a value in the range of 0.2 to 0.5.
 5. The device for generating alternative of a normal brain database according to claim 4, wherein the risk α is set to a value in the range of 0.2 to 0.4.
 6. The device for generating alternative of a normal brain database according to claim 1, further comprising means for selecting, based on normalized and anatomically standardized brain functional image of each of the subjects, representative values from status values of regions from the brain surface to a predetermined vertical depth thereinto, and generating a brain surface state image of each of the subjects using the representative values as status values of the brain surface, wherein the regions with a status value in the brain surface state image are used as the regions for use in determining a brain disease.
 7. The device for generating alternative of a normal brain database according to claim 1, wherein the brain functional images are brain bloodstream images indicating a bloodstream associated value.
 8. The device for generating alternative of a normal brain database according to claim 1, wherein the generating means calculates, based on brain functional images from which status values presumed to indicate a brain disease have been rejected, mean status value and standard deviation for each of the brain regions for use in determining a brain disease to generate a database indicating the normal state.
 9. A device for generating alternative of normal brain surface database to be used for diagnosing disease, based on brain bloodstream images of subjects including patients which show bloodstream associated value of each points of brain, the device comprising: means for normalizing bloodstream associated value at each point of each brain bloodstream image of subject based on bloodstream associated values of a part or whole of brain bloodstream image of the subject; means for spatially transforming each brain bloodstream image of subject in accordance with anatomical standard brain; means for generating brain surface bloodstream image of each subjects based on said anatomically transformed and normalized brain bloodstream images of subjects, said brain surface bloodstream image having surface bloodstream associated value of each surface points, said surface bloodstream associated value being selected as representative value of bloodstream associated values from brain surface to predetermined depth perpendicular to the brain surface; and means for rejecting bloodstream associated values presumed as indicating disease with regard to each points of brain generated by brain surface bloodstream image generating means, means for generating the alternative of normal brain database by obtaining at least mean status value for said each point of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain surface bloodstream images of subjects in which said bloodstream values presumed as indicating disease are rejected, wherein the rejecting means calculates the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extreme extream studentized deviate), and rejects at least the status value selected as the ESD when the ESD exceeds a critical value λ calculated based on a predetermined risk α.
 10. A non-transitory medium storing a computer program for realizing, by a computer, a device for generating alternative of normal brain surface database to be used for diagnosing disease, based on brain status images of subjects including patients, the alternative of normal brain database showing normal status of each points of brain, the program realizing by the computer: means for normalizing status value at each point of each brain bloodstream image of subject based on status values of a part or whole of brain status image of the subject; means for spatially transforming each brain status image of subject in accordance with anatomical standard brain; means for rejecting status values presumed as indicating disease with regard to each points of brain generated by brain status image generating means, means for generating the alternative of normal brain database by obtaining at least mean status value for said each point of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain status images of subjects in which said status values presumed as indicating disease are rejected, wherein the rejecting means calculates the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extream studentized deviate), and rejects at least the status value selected as the ESD when the ESD exceeds a critical value λ calculated based on a predetermined risk α.
 11. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 10, wherein the rejecting means, for each points, i) forms a target status value group consisting of the status values of all the plurality of subjects, ii) calculates the ESD of the target status value group and stores the ESD as a deviated status value, iii) determines whether or not the ESD exceeds a critical value λ and stores the result of determination in a correlated manner with the deviated status value, iv) rejects the deviated status value from the target status value group to form a new target status value group and performs the steps ii) to iv) on the new target status value group when the number of repetitions has not reached a predetermined value, and v) determines all of the deviated status values determined before the ESD exceeds the critical value λ as targets of rejection when the number of repetitions reaches the predetermined value.
 12. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 10, wherein the risk α is set to a value in the range of 0.1 to 0.6.
 13. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 12, wherein the risk α is set to a value in the range of 0.2 to 0.5.
 14. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 13, wherein the risk α is set to a value in the range of 0.2 to 0.4.
 15. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 10, further realizing: means for selecting, based on normalized and anatomically standardized brain functional image of each of the subjects, representative values from status values of regions from the brain surface to a predetermined vertical depth thereinto, and generating a brain surface state image of each of the subjects using the representative values as status values of the brain surface, wherein the regions with a status value in the brain surface state image are used as the regions for use in determining a brain disease.
 16. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 10, wherein the brain functional images are brain bloodstream images indicating a bloodstream associated value.
 17. The non-transitory medium storing a program for generating alternative of a normal brain database according to claim 10, wherein the generating means calculates, based on brain functional images from which status values presumed to indicate a brain disease have been rejected, mean status value and standard deviation for each of the brain regions for use in determining a brain disease to generate α database indicating the normal state.
 18. A non-transitory medium storing a computer program, by a computer, for realizing a device for generating alternative of normal brain surface database to be used for diagnosing disease, based on brain bloodstream images of subjects including patients which show bloodstream associated value of each points of brain, the program realizing by the computer: means for normalizing bloodstream associated value at each point of each brain bloodstream image of subject based on bloodstream associated values of a part or whole of brain bloodstream image of the subject; means for spatially transforming each brain bloodstream image of subject in accordance with anatomical standard brain; means for generating brain surface bloodstream image of each subjects based on said anatomically transformed and normalized brain bloodstream images of subjects, said brain surface bloodstream image having surface bloodstream associated value of each surface points, said surface bloodstream associated value being selected as representative value of bloodstream associated values from brain surface to predetermined depth perpendicular to the brain surface; and means for rejecting bloodstream associated values presumed as indicating disease with regard to each points of brain generated by brain surface bloodstream image generating means, means for generating the alternative of normal brain surface database by obtaining at least mean status value for said each point of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain surface bloodstream images of subjects in which said bloodstream values presumed as indicating disease are rejected, wherein the rejecting means calculates the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extream studentized deviate), and rejects at least the status value selected as the ESD when the ESD exceeds a critical value λ calculated based on a predetermined risk α.
 19. A method for generating alternative of normal brain database to be used for diagnosing brain disease based on brain status images of subjects including patients, the alternative of normal brain database showing normal status of each points of brain, the method comprising the steps of: normalizing status value at each point of each brain status image of subject based on status values of a part or whole of brain status image of the subject; spatially transforming each brain status image of subject in accordance with anatomical standard brain; rejecting status values presumed as indicating disease with regard to each points of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain status images of subjects; and generating the alternative of normal brain database by obtaining at least mean status value for said each point of brain to be used for diagnosing disease based on said anatomically transformed and normalized brain status images of subjects in which said status values presumed as indicating disease are rejected, wherein, in the rejecting step, the maximum value of deviation/standard deviation of status values of the plurality of subjects for each points as an ESD (extream studentized deviate) is calculated, and at least the status value selected as the ESD is rejected when the ESD exceeds a critical value λ calculated based on a predetermined risk α. 